Evidence map›Paper›PMID 42572574›Full record

ArticleiScience2026

Digital transformation and green total factor productivity: A contingency perspective on spatial and social embeddedness.

Yu Chen, Shuangshuang Liu, Qian Zhou

Abstract read
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yu ChenSchool of Economics and Management, Zhengzhou University of Light Industry, ZhengZhou 450000, China.
Shuangshuang LiuSchool of Economics and Management, Northwest University, Xi'an 710127, China.
Qian ZhouSchool of Economics, Anhui University, HeFei 230601, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Green high-quality development requires balancing environmental sustainability with manufacturing growth. Using data from Chinese publicly listed firms from 2012 to 2021, this study demonstrates that digital transformation (DT) significantly increases green total factor productivity (GTFP) in the manufacturing sector. This finding remains robust across endogeneity and specification tests, including instrumental variable approaches and Heckman selection models. Mechanism analysis reveals that both market competition and public environmental concern positively moderate the DT-GTFP nexus. Heterogeneity analysis indicates that this positive impact is more pronounced in the eastern regions, during the early stages of DT, and among small-scale enterprises and non-heavily polluting industries. This study highlights the critical role of manufacturing DT in driving green development and offers flexible policy implications for aligning industrial upgrading with public aspirations for a higher quality of life.

Indexed as

digital transformationDTgreen total factor productivityGTFPmanufacturing firmsmarket competitivenesspublic environmental concern

Identifiers

PMID42572574
PMCPMC13453038

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.